正则化超声Lanczos反卷积的NCB成像分析  

The Imaging Analysis for Normalization Supersonic Lanczos Deconvolution by NCB Algorithm

作　　者：罗晓华[1] 

机构地区：[1]重庆交通大学图书馆,重庆400074

出　　处：《半导体光电》2015年第4期672-676,共5页Semiconductor Optoelectronics

基　　金：重庆市教委科学技术研究项目(kj110119)

摘　　要：Lanczos方法是求解大尺度逆问题的一种有效方法,这种方法的特点是可以把大尺度问题转化为小尺度问题,而且可以把解严格限制在Krylov子空间,只是它存在的半收敛性问题需要进一步克服。为了确保算法的有效性、稳定性和精确性,Lanczos混合法(Lanczos-hybrid)试图通过正则参数的适当选取来解决这个问题。文章在Hansen提出的正则化参数选取的NCP方法基础上,设计了一种新的算法NCB,即利用Burg功率谱代替NCP中的经典周期图谱,较好地克服了Lanczos的半收敛性问题,降低了解对迭代次数的敏感性,得到了大尺度反卷积病态问题的稳定解;并以超声RF信号为例进行仿真,结果表明,NCB的成像效果比GCV要好。The Lanczos method, which can restrict the solution to Krylov subspace precisely, is an effective method to solve the large-scale inverse problem, but the problem of semi-convergence needs to be overcome. To ensure the validity, the stability and the precision of the solution, Lanczos-hybrid can be used to solve this semi-convergence problem through the right selection of a regularization parameters. Based on NCP （The Normalized Cumulative Periodogram normalization accumulation frequency spectrum ） method proposed by Hansen in 2006 for the selection of a regularization parameters, designed is a new algorithm NCB, in which the Burg power spectrum replaces classical periodogram. With the new method, the semi- convergence problem existing in the Lanczos method is solved and the sensitivity of the solution to the number of iterations is reduced, and the stable solution for the large-scale inverse problem is obtained. Taking the ultrasonic RF signal as an example, the imaging effect for a variable method was analyzed, and the results show that the NCB is better than GCV.

关 键 词：信号处理 反卷积 病态问题 Lanczos双对角化 NCB方法 

分 类 号：TN391.41[电子电信—物理电子学]